After two years of nightly testing, we’ve come to learn the power and limitations of Jepsen as a testing tool.
When IT experts consider high availability infrastructure for mission-critical services, their minds often leap to Oracle as the preeminent service provider. But Oracle's database was designed in a pre-cloud world. As a cloud native database, CockroachDB introduces a new way of providing always-on availability, strong data consistency, and distributed performance.
This updated report includes the reproduction steps to our Cloud Report findings in which we benchmakred AWS and GCP. These steps allow for copy-paste reproduction of our process to empower you to check our work.
How we built a 40x faster hash joiner using vectorized execution.
CockroachDB's Consistency Model fits somewhere between serializability and linerarizability. We're proposing a new marketing phrase for CRDB's guarantees: no stale reads.
CockroachDB uses RocksDB for its storage engine because of RocksDB's rich feature set, which is necessary for a complex product like a distributed SQL database.
This post introduces Transactional Pipelining which dramatically speeds up distributed transactions with respect to the latency cost of distributed consensus.
This blog covers the practical experience of running a distributed system across multiple Kubernetes clusters including what makes it challenging and what solutions are available (some of which we run in production).
Customers rely on us to help navigate the complexities of the increasingly competitive cloud wars. This inspired the 2018 Cloud Computing Report, where we benchmark performance, latency, CPU, network, I/O, and cost of AWS and GCP.
CockroachDB 2.1 is 50x more scalable than Amazon Aurora at less than 2% of the price per tpmC. Read on to see performance benchmarks of CockroachDB 2.1, including the latest TPC-C results.